Definitions[]
AI system lifecycle
| “ | phases involve: i) 'design, data and models'; which is a context-dependent sequence encompassing planning and design, data collection and processing, as well as model building; ii) 'verification and validation'; iii) 'deployment'; and iv) 'operation and monitoring'. These phases often take place in an iterative manner and are not necessarily sequential. The decision to retire an AI system from operation may occur at any point during the operation and monitoring phase.[1] | ” |
| “ | [is a] set of phases concerning an AI system that involve: (i) planning and design, data collection and processing, and model building; (ii) verification and validation; (iii) deployment; (iv) operation and monitoring; and (v) End-of-life.[2] | ” |